Improving Open Intent Detection via Triplet-Contrastive Learning and Adaptive Boundary

Open intent detection is a critical task within dialogue systems, aiming to effectively classify known intents while also identifying unknown intents that have not been encountered in the training data. Learning discriminative representations and precise decision boundary are two key challenges in open intent detection. To address these challenges, this paper proposes a Triplet-Contrastive representation learning strategy and an Adaptive Boundary (TCAB) method. Traditional methods often confine the features of known intents to compact regions, assuming that open intents exist outside these regions. Nevertheless, open intents can be dispersed within the known intents. To tackle this issue, this paper introduces a triplet-contrastive representation learning method to acquire discriminative semantic features and distinguish between similar open intents and known intents. Additionally, to achieve more precise decision boundaries, an adaptive boundary method takes into account both in-class and out-of-class instances for calibrating boundary radius. Comprehensive experiments demonstrate that our approach yields substantial improvements over a range of baseline methods on three benchmark datasets. Our code is available at
https://github.com/cgh-code777/TCAB
.